With the expanding reach of computer networks and proliferation of connected devices, data collection entities (e.g., market research organizations) can collect a tremendous volume and variety of data related to sales or use of products in the market place from a wider range of sources than before (e.g., from point-of-sale (POS) systems, online transactions, social media, loyalty programs, call center records, etc.). The collected data may be multi-dimensional and also include multiple attributes for each data dimension. End-users (e.g., product manufacturers, merchandisers or retailers, etc.) can, in principle, analyze this data to generate valuable insights for improving the effectiveness of marketing campaigns, optimizing assortment and merchandising decisions, and removing inefficiencies in distribution and operations. However, it may not be necessary to analyze all of the large volumes and variety of the multiple attribute and multi-dimensional data that the end-users can receive from the data collection entities to extract meaningful or relevant content for the end-users' purposes. Further, storing all of the large volumes and variety of data that the end-users can receive (e.g., automatically or on-line) from the data collection entities may be also be unnecessary and burdensome.
Consideration is being given to systems and methods for consolidating or structuring multiple attribute and multi-dimensional data for storage or for further data analytics.